One of the biggest challenges manufacturers face today, and have had to contend with historically, is the lack of visibility into their products once they’ve left the factory. As a consequence of this, products have been considered the end of the value chain. But as customer demand for greater product reliability and performance has grown, manufacturers have begun to invest in smart, connected products as a way to experiment with generating valuable data for their manufacturing, supply chain and engineering teams.

Connected products have shifted from being mere experimental initiatives to a core part of many companies’ business models. Manufacturers are using the data they’re ingesting from these products to create value across operational insight, product development, customer experience and revenue growth. And that means access to that data is more crucial than ever before.

As manufacturers absorb more and more intelligence from their products, they’ve realized that amassing petabytes of data is only part of the equation — democratizing that data, and ensuring that teams across the organization have access to it and can learn from it, can be an important differentiator between being an industry innovator and struggling to keep up with the competition. Manufacturing leaders are moving beyond simply asking what happened with their products to why it happened — and they’re being empowered with Snowflake Intelligence.

They’re improving product performance and customer experience and developing new revenue streams by ensuring that invaluable product and customer data gets to all of the relevant teams in the business to iterate and improve upon. Business users close to this product data can now get valuable insights from deeply intelligent agents that blend structured and unstructured data and real-time context to suggest actionable next steps. And forward-looking companies such as Toyota Motor Europe (TME) are already leveraging this technology to make smarter, more informed business decisions with major impact.

Spotlight: Toyota Motor Europe — innovating, iterating and developing with Snowflake Intelligence

TME’s north star is finding ways to bring the voice of the customer closer to the heart of its product design and car strategies, such as defining model specifications or anticipating future customer needs. However, with over 100 fragmented systems housing customer data ranging from vehicle data to sales records and beyond, getting it all in one place to extract valuable insights from the data was a challenge.

The dashboards that planners had historically relied on were no longer effective at helping them drill down to those insights, as their level of depth and complexity limited flexibility. TME needed a more intelligent solution to replace those dashboards, one with an AI assistant that TME’s employees could communicate with via natural language and which could mirror the team’s deep industry knowledge and complex logic. The team needed answers in seconds — not hours or days.

While TME initially decided to build a fully custom solution, the process took many months, and the agent required substantial training and effort to provide accurate feedback due to the intricacy of the company’s business rules. TME needed something better. It turned to Snowflake Intelligence instead, and in TME’s evaluation, within about a month the solution had achieved a comparable level of accuracy and functionality as TME’s custom solution.

With the ability to analyze all of TME’s data holistically, Snowflake Intelligence empowers manufacturers to go beyond surface-level reporting and uncover the drivers behind performance. TME’s product planners are now able to understand patterns and trends across their disparate data sources while still supporting governance, accuracy and scalability. And with the aid of Snowflake Cortex Analyst, they can map relationships between data and business concepts while verified queries and custom instructions interpret intent while operating within the parameters of established business rules.

In addition, Snowflake Cortex Search utilizes a business glossary to handle domain terms and synonyms to provide rich context, while an orchestration layer chooses the right tools and standardizes outputs to achieve clear, structured responses.

In order for any team to feel confident in its AI assistant, it has to establish trust via highly accurate responses and consistency. In a controlled evaluation, TME’s solution built on Snowflake Intelligence achieved 87% business accuracy (as defined by TME’s internal validation criteria),demonstrated a strong grasp of business context and terminology, and provided consistent responses. And in starting small, validating and then scaling, and having transparency into the assistant’s reasoning and sources, TME’s planners were able to feel more assurance about the potential to use the solution more widely across the business or for other future generative AI initiatives.

TME’s planning team can now leverage the power of Snowflake Intelligence to focus their efforts on taking customer and product feedback and applying it to vehicle improvements and innovations — with faster, more insight-driven decision-making for improved business outcomes and value.

Smart products, smarter manufacturing organizations

It’s important to keep in mind that product data alone doesn’t create value. Connected products depend on a unified data foundation to develop product strategies that integrate with manufacturing and quality data, supply chain and parts availability, and customer, contract and service systems. Manufacturers who ensure their data is AI ready will be in a much stronger position to democratize their data across the organization and bring it all into one source of truth while maintaining existing security, compliance and privacy controls. Eliminating data silos and fragmentation with Snowflake Intelligence helps customer and product data reach the internal teams that need it most to iterate, innovate and improve to unlock new revenue opportunities and assure the customer that they’re heard — and their product teams are listening.

See Toyota Motor Europe’s full story

TME’s experience demonstrates how unifying product data across disparate sources under a single source of truth can fundamentally transform business operations and product design. To read more about TME’s journey with Snowflake Intelligence, read our blog.